Summary of Ram2c: a Liberal Arts Educational Chatbot Based on Retrieval-augmented Multi-role Multi-expert Collaboration, by Haoyu Huang et al.
RAM2C: A Liberal Arts Educational Chatbot based on Retrieval-augmented Multi-role Multi-expert Collaboration
by Haoyu Huang, Tong Niu, Rui Yang, Luping Shi
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework called Retrieval-augmented Multi-role Multi-expert Collaboration (RAM2C) to generate educational dialogues that meet human standards. The framework is designed to balance Humanized communication, Teaching expertise, and Safety-ethics (HTS) in liberal arts dialogues. To overcome the limitations of existing large language models (LLMs), RAM2C organizes LLMs into multi-expert groups with distinct roles, retrieving knowledge from HTS-guided knowledge bases encompassing teaching skills, psychology, and safety ethics. The framework fine-tunes LLMs using a dataset generated through RAM2C, achieving high-quality educational dialogues that excel in Chinese reading teaching tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make computers understand how to talk about important topics like education. They want to make sure the computer conversations are helpful and safe for people learning. To do this, they use special computer models that learn from lots of conversations about teaching. These models work together to create new conversations that are even better than before. They tested these conversations with teachers and found that they were very good at helping students learn. |
